Senior Data Engineer 7 - Data Modeler

Job Summary:

Leads projects for design, development and maintenance of a data and analytics platform. Effectively and efficiently process, store and make data available to analysts and other consumers. Works with key business stakeholders, IT experts and subject-matter experts to plan, design and deliver optimal analytics and data science solutions. Works on one or many product teams at a time.

Key Responsibilities:

Designs and automates deployment of our distributed system for ingesting and transforming data from various types of sources (relational, event-based, unstructured). Designs and implements framework to continuously monitor and troubleshoot data quality and data integrity issues. Implements data governance processes and methods for managing metadata, access, retention to data for internal and external users. Designs and provide guidance on building reliable, efficient, scalable and quality data pipelines with monitoring and alert mechanisms that combine a variety of sources using ETL/ELT tools or scripting languages. Designs and implements physical data models to define the database structure. Optimizing database performance through efficient indexing and table relationships. Participates in optimizing, testing, and troubleshooting of data pipelines. Designs, develops and operates large scale data storage and processing solutions using different distributed and cloud based platforms for storing data (e.g. Data Lakes, Hadoop, Hbase, Cassandra, MongoDB, Accumulo, DynamoDB, others). Uses innovative and modern tools, techniques and architectures to partially or completely automate the most-common, repeatable and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. Assists with renovating the data management infrastructure to drive automation in data integration and management. Ensures the timeliness and success of critical analytics initiatives by using agile development technologies such as DevOps, Scrum, Kanban Coaches and develops less experienced team members.

Core Responsibilities Unique to the Role 
1) Lead the design, governance, and continuous evolution of enterprise data models and data products, including conceptual, logical, and physical data modelling. Review and approve data model changes, new source integrations, tables, attributes, and relationships introduced across data products to ensure alignment with enterprise data architecture standards, modelling principles, scalability, reusability, and business requirements.
2) Design and build reliable, high-performance data engineering solutions including data ingestion, transformation, integration, and orchestration pipelines across cloud data platforms, ensuring data quality, security, governance, scalability, and operational excellence throughout the enterprise data lifecycle across Supply Chain, Quality, Finance, Product Lifecycle, and other Enterprise Products domains.
3) Partner with Data Engineers, Solution Engineers, Analysts, and Business Stakeholders to translate business requirements into governed, discoverable, and AI-ready data products by applying Data-as-a-Product principles, metadata standards, lineage, semantic modelling, and data governance practices that enable trusted enterprise data 
 

Required Skills, Education, or Experience
1) 8+ years of strong experience in enterprise data modelling and data engineering, supporting large-scale data warehouses, lakehouses, semantic models, and enterprise data products on modern cloud platforms.
2) Strong expertise in enterprise data modelling, including conceptual, logical, and physical data models, dimensional modelling (Star/Snowflake), Data Vault, normalized and canonical models, semantic modelling, master data management, and enterprise data architecture standards.
3) Proven experience governing and reviewing enterprise data models across multiple data products, including evaluation of new source systems, entities, attributes, relationships, and model enhancements to ensure alignment with enterprise architecture principles, data standards, scalability, reusability, and long-term maintainability.
4) Experience designing and implementing data solutions across the complete data lifecycle, including Source Systems, Data Ingestion, Raw/Bronze, Curated/Silver, Business/Gold, Semantic Layers, and Enterprise Data Products, ensuring data quality, governance, security, performance, and operational excellence.
5) Strong hands-on expertise in data engineering and cloud data platforms, including Snowflake, Databricks, SQL, Matillion, dbt,Power BI  with experience building scalable, high-performance data pipelines, data integration solutions, and reusable enterprise data assets.
6) Proven ability to lead technical design and data model reviews and collaborate with Solution Engineers, Data Engineers, Architects, and Business Stakeholders to translate business requirements into scalable data products and establish enterprise-wide data modelling, engineering, and governance best practices.
 

Preferred (Nice to Have) Skills, Education, or Experience 
1)Experience working within a Data-as-a-Product operating model, including enterprise data modelling, semantic layer design, data cataloging, metadata management, data lineage, data product certification, governance practices, and development of reusable data assets supporting analytics and self-service consumption.
2) Experience supporting AI-ready data platforms and advanced analytics initiatives, including GenAI, AI/ML, Retrieval-Augmented Generation (RAG), vector databases, semantic search, knowledge management, and scalable data engineering and data modelling practices that enable trusted enterprise AI solutions.

        
Education
Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, Information Systems, or related field. Master's degree preferred.

Competencies: 
System Requirements Engineering  - Uses appropriate methods and tools to translate stakeholder needs into verifiable requirements to which designs are developed; establishes acceptance criteria for the system of interest through analysis, allocation and negotiation; tracks the status of requirements throughout the system lifecycle; assesses the impact of changes to system requirements on project scope, schedule, and resources; creates and maintains information linkages to related artifacts.

Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.

Communicates effectively - Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.

Customer focus - Building strong customer relationships and delivering customer-centric solutions.

Decision quality - Making good and timely decisions that keep the organization moving forward.

Data Extraction - Performs data extract-transform-load (ETL) activities from variety of sources and transforms them for consumption by various downstream applications and users using appropriate tools and technologies.

Programming - Creates, writes and tests computer code, test scripts, and build scripts using algorithmic analysis and design, industry standards and tools, version control, and build and test automation to meet business, technical, security, governance and compliance requirements.

Quality Assurance Metrics - Applies the science of measurement to assess whether a solution meets its intended outcomes using the IT Operating Model (ITOM), including the SDLC standards, tools, metrics and key performance indicators, to deliver a quality product.

Solution Documentation - Documents information and solution based on knowledge gained as part of product development activities; communicates to stakeholders with the goal of enabling improved productivity and effective knowledge transfer to others who were not originally part of the initial learning.

Solution Validation Testing - Validates a configuration item change or solution using the Function's defined best practices, including the Systems Development Life Cycle (SDLC) standards, tools and metrics, to ensure that it works as designed and meets customer requirements.

Data Quality - Identifies, understands and corrects flaws in data that supports effective information governance across operational business processes and decision making.

Problem Solving - Solves problems and may mentor others on effective problem solving by using a systematic analysis process by leveraging industry standard methodologies to create problem traceability and protect the customer; determines the assignable cause; implements robust, data-based solutions; identifies the systemic root causes and ensures actions to prevent problem reoccurrence are implemented.

Values differences - Recognizing the value that different perspectives and cultures bring to an organization. 

Education, Licenses, Certifications: 
College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. This position may require licensing for compliance with export controls or sanctions regulations. 

Experience: 
Intermediate experience in a relevant discipline area is required. Knowledge of the latest technologies and trends in data engineering are highly preferred and includes:
- Familiarity analyzing complex business systems, industry requirements, and/or data regulations
- Background in processing and managing large data sets
- Design and development for a Big Data platform using open source and third-party tools
- SPARK, Scala/Java, Map-Reduce, Hive, Hbase, and Kafka or equivalent college coursework
- SQL query language
- Clustered compute cloud-based implementation experience
- Experience developing applications requiring large file movement for a Cloud-based environment and other data extraction tools and methods from a variety of sources
- Experience in building analytical solutions 
Intermediate experiences in the following are preferred:
- Experience with IoT technology 
- Experience in Agile software development